{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、前期工作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "今天的东西可能过于硬核！你准备好了吗？⏳"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🔥本文 GitHub [https://github.com/kzbkzb/Python-AI](https://github.com/kzbkzb/Python-AI) 已收录\n",
    "\n",
    "- 作者：[K同学啊](https://mp.weixin.qq.com/s/NES9RhtAhbX_jsmGua28dA)\n",
    "- 来自专栏：《深度学习100例》-Tensorflow2版本\n",
    "- 数据链接：https://pan.baidu.com/s/1Yw0ifbysm-z1nq7Gr4rW9Q （提取码：t4bk）\n",
    "\n",
    "我的环境：\n",
    "\n",
    "- 语言环境：Python3.6.5\n",
    "- 编译器：jupyter notebook\n",
    "- 深度学习环境：TensorFlow2.4.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 设置GPU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果使用的是CPU可以注释掉这部分的代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "gpus = tf.config.list_physical_devices(\"GPU\")\n",
    "\n",
    "if gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpus[0], True)  #设置GPU显存用量按需使用\n",
    "    tf.config.set_visible_devices([gpus[0]],\"GPU\")\n",
    "\n",
    "# 打印显卡信息，确认GPU可用\n",
    "print(gpus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "import os,PIL\n",
    "\n",
    "# 设置随机种子尽可能使结果可以重现\n",
    "import numpy as np\n",
    "np.random.seed(1)\n",
    "\n",
    "# 设置随机种子尽可能使结果可以重现\n",
    "import tensorflow as tf\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "import pathlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"D:/jupyter notebook/DL-100-days/datasets/016_Pokemon\"\n",
    "\n",
    "data_dir = pathlib.Path(data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "图片总数为： 219\n"
     ]
    }
   ],
   "source": [
    "image_count = len(list(data_dir.glob('*/*')))\n",
    "\n",
    "print(\"图片总数为：\",image_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 二、数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 加载数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用`image_dataset_from_directory`方法将磁盘中的数据加载到`tf.data.Dataset`中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 8\n",
    "img_height = 224\n",
    "img_width = 224"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow版本是2.2.0的同学可能会遇到`module 'tensorflow.keras.preprocessing' has no attribute 'image_dataset_from_directory'`的报错，升级一下TensorFlow就OK了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 219 files belonging to 10 classes.\n",
      "Using 176 files for training.\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "关于image_dataset_from_directory()的详细介绍可以参考文章：https://mtyjkh.blog.csdn.net/article/details/117018789\n",
    "\"\"\"\n",
    "train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    data_dir,\n",
    "    validation_split=0.2,\n",
    "    subset=\"training\",\n",
    "    seed=12,\n",
    "    image_size=(img_height, img_width),\n",
    "    batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 219 files belonging to 10 classes.\n",
      "Using 43 files for validation.\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "关于image_dataset_from_directory()的详细介绍可以参考文章：https://mtyjkh.blog.csdn.net/article/details/117018789\n",
    "\"\"\"\n",
    "val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    data_dir,\n",
    "    validation_split=0.2,\n",
    "    subset=\"validation\",\n",
    "    seed=12,\n",
    "    image_size=(img_height, img_width),\n",
    "    batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以通过class_names输出数据集的标签。标签将按字母顺序对应于目录名称。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Alcremie', 'Eevee', 'Furfrou', 'Kyurem', 'Minior', 'Pikachu', 'Rotom', 'Squirtle', 'Vivillon', 'Zygarde']\n"
     ]
    }
   ],
   "source": [
    "class_names = train_ds.class_names\n",
    "print(class_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 可视化数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))  # 图形的宽为10高为5\n",
    "plt.suptitle(\"数据展示\")\n",
    "\n",
    "for images, labels in train_ds.take(1):\n",
    "    for i in range(8):\n",
    "        \n",
    "        ax = plt.subplot(2, 4, i + 1)  \n",
    "        \n",
    "        ax.patch.set_facecolor('yellow')\n",
    "        \n",
    "        plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
    "        plt.title(class_names[labels[i]])\n",
    "        \n",
    "        plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 3. 再次检查数据 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 224, 224, 3)\n",
      "(8,)\n"
     ]
    }
   ],
   "source": [
    "for image_batch, labels_batch in train_ds:\n",
    "    print(image_batch.shape)\n",
    "    print(labels_batch.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `Image_batch`是形状的张量（8, 224, 224, 3)。这是一批形状240x240x3的8张图片（最后一维指的是彩色通道RGB）。 \n",
    "- `Label_batch`是形状（8，）的张量，这些标签对应8张图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 配置数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **shuffle()** ： 打乱数据，关于此函数的详细介绍可以参考：https://zhuanlan.zhihu.com/p/42417456\n",
    "- **prefetch()** ：预取数据，加速运行，其详细介绍可以参考我前两篇文章，里面都有讲解。\n",
    "- **cache()** ：将数据集缓存到内存当中，加速运行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "AUTOTUNE = tf.data.AUTOTUNE\n",
    "\n",
    "train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)\n",
    "val_ds   = val_ds.cache().prefetch(buffer_size=AUTOTUNE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、调用官方网络模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前面给大家介绍了那么多模型，究竟哪一个模型更好呢？要不我们一 一手动复现+运行一下？NO，工作量太大了，咱头发也不够用啊\n",
    "\n",
    "不知道你是否听说一个叫 `tf.keras.applications` 的神器，就是我前面用到的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "简单的讲 `tf.keras.applications` 就是它把我们常用的一些模型进行了封装，我如果想调用某一个模型时（例如：VGG-16），直接调用函数接口就OK了，而且还可以直接加载他们的预训练权重进行迁移学习（迁移学习：顾名思义就是就是把已学训练好的模型参数迁移到新的模型来帮助新模型训练。）\n",
    "\n",
    "`tf.keras.applications` 目前支持的模型及该模型的性能参数如下（知道这么回事就好）："
   ]
  },
  {
   "attachments": {
    "image-3.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image-3.png](attachment:image-3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上述的参数来自 ILSVRC 大赛，使用的是 ImageNet 数据集。\n",
    "\n",
    "`TOP-5正确率` 是什么意思：举个例子，我们要用训练好的网络进行分类任务，假设一共有50类图片，我们输入一张图片进行测试时，网络会依次输出该图片为这50个类别的概率。TOP-5正确率就是正确的标签（分类）在这5个类别里之中的概率。计算公式为：`所有测试图片中正确标签在前五个分类概率的个数` / `所有的测试图片数`（这部分内容了解即可）\n",
    "\n",
    "`tf.keras.applications` API地址：https://www.tensorflow.org/api_docs/python/tf/keras/applications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**接口示例：VGG-16官方模型接口**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.engine.functional.Functional at 0x220df3e35f8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这是函数原型，请勿运行该代码\n",
    "tf.keras.applications.vgg16.VGG16(\n",
    "    include_top=True, weights='imagenet', input_tensor=None,\n",
    "    input_shape=None, pooling=None, classes=1000,\n",
    "    classifier_activation='softmax'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "常用的三个参数解释如下：\n",
    "\n",
    "- include_top：是否包括网络顶部的 3 个全连接层。\n",
    "- weights：默认不加载权重文件，`\"imagenet\" `加载官方权重文件，或者输入自己的权重文件路径。\n",
    "- classes：分类图像的类别数\n",
    "\n",
    "其他参数暂时不建议大家了解，模型接口都是非常相似的，大家可以从上面的众多模型中选择自己想要的调用，接口函数如下：\n",
    "\n",
    "1. tf.keras.applications.xception.Xception()\n",
    "2. tf.keras.applications.vgg16.VGG16()\n",
    "3. tf.keras.applications.vgg19.VGG19()\n",
    "4. tf.keras.applications.resnet50.ResNet50()\n",
    "5. tf.keras.applications.inception_v3.InceptionV3()\n",
    "6. tf.keras.applications.inception_resnet_v2.InceptionResNetV2()\n",
    "7. tf.keras.applications.mobilenet.MobileNet()\n",
    "8. tf.keras.applications.mobilenet_v2.MobileNetV2()\n",
    "9. tf.keras.applications.densenet.DenseNet121()\n",
    "10. tf.keras.applications.densenet.DenseNet169()\n",
    "11. tf.keras.applications.densenet.DenseNet201()\n",
    "12. tf.keras.applications.nasnet.NASNetMobile()\n",
    "13. tf.keras.applications.nasnet.NASNetLarge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"densenet121\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d (ZeroPadding2D)  (None, 230, 230, 3)  0           input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1/conv (Conv2D)             (None, 112, 112, 64) 9408        zero_padding2d[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1/bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1/conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1/relu (Activation)         (None, 112, 112, 64) 0           conv1/bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_1 (ZeroPadding2D (None, 114, 114, 64) 0           conv1/relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool1 (MaxPooling2D)            (None, 56, 56, 64)   0           zero_padding2d_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_bn (BatchNormali (None, 56, 56, 64)   256         pool1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_relu (Activation (None, 56, 56, 64)   0           conv2_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 128)  8192        conv2_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_bn (BatchNormali (None, 56, 56, 128)  512         conv2_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_relu (Activation (None, 56, 56, 128)  0           conv2_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 32)   36864       conv2_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_concat (Concatenat (None, 56, 56, 96)   0           pool1[0][0]                      \n",
      "                                                                 conv2_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_0_bn (BatchNormali (None, 56, 56, 96)   384         conv2_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_0_relu (Activation (None, 56, 56, 96)   0           conv2_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_conv (Conv2D)    (None, 56, 56, 128)  12288       conv2_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_bn (BatchNormali (None, 56, 56, 128)  512         conv2_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_relu (Activation (None, 56, 56, 128)  0           conv2_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 32)   36864       conv2_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_concat (Concatenat (None, 56, 56, 128)  0           conv2_block1_concat[0][0]        \n",
      "                                                                 conv2_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_0_bn (BatchNormali (None, 56, 56, 128)  512         conv2_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_0_relu (Activation (None, 56, 56, 128)  0           conv2_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_conv (Conv2D)    (None, 56, 56, 128)  16384       conv2_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_bn (BatchNormali (None, 56, 56, 128)  512         conv2_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_relu (Activation (None, 56, 56, 128)  0           conv2_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_conv (Conv2D)    (None, 56, 56, 32)   36864       conv2_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_concat (Concatenat (None, 56, 56, 160)  0           conv2_block2_concat[0][0]        \n",
      "                                                                 conv2_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_0_bn (BatchNormali (None, 56, 56, 160)  640         conv2_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_0_relu (Activation (None, 56, 56, 160)  0           conv2_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_conv (Conv2D)    (None, 56, 56, 128)  20480       conv2_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_bn (BatchNormali (None, 56, 56, 128)  512         conv2_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_relu (Activation (None, 56, 56, 128)  0           conv2_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_2_conv (Conv2D)    (None, 56, 56, 32)   36864       conv2_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_concat (Concatenat (None, 56, 56, 192)  0           conv2_block3_concat[0][0]        \n",
      "                                                                 conv2_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_0_bn (BatchNormali (None, 56, 56, 192)  768         conv2_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_0_relu (Activation (None, 56, 56, 192)  0           conv2_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_conv (Conv2D)    (None, 56, 56, 128)  24576       conv2_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_bn (BatchNormali (None, 56, 56, 128)  512         conv2_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_relu (Activation (None, 56, 56, 128)  0           conv2_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_2_conv (Conv2D)    (None, 56, 56, 32)   36864       conv2_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_concat (Concatenat (None, 56, 56, 224)  0           conv2_block4_concat[0][0]        \n",
      "                                                                 conv2_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_0_bn (BatchNormali (None, 56, 56, 224)  896         conv2_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_0_relu (Activation (None, 56, 56, 224)  0           conv2_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_conv (Conv2D)    (None, 56, 56, 128)  28672       conv2_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_bn (BatchNormali (None, 56, 56, 128)  512         conv2_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_relu (Activation (None, 56, 56, 128)  0           conv2_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_2_conv (Conv2D)    (None, 56, 56, 32)   36864       conv2_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_concat (Concatenat (None, 56, 56, 256)  0           conv2_block5_concat[0][0]        \n",
      "                                                                 conv2_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "pool2_bn (BatchNormalization)   (None, 56, 56, 256)  1024        conv2_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "pool2_relu (Activation)         (None, 56, 56, 256)  0           pool2_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool2_conv (Conv2D)             (None, 56, 56, 128)  32768       pool2_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool2_pool (AveragePooling2D)   (None, 28, 28, 128)  0           pool2_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_bn (BatchNormali (None, 28, 28, 128)  512         pool2_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_relu (Activation (None, 28, 28, 128)  0           conv3_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  16384       conv3_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 32)   36864       conv3_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_concat (Concatenat (None, 28, 28, 160)  0           pool2_pool[0][0]                 \n",
      "                                                                 conv3_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_0_bn (BatchNormali (None, 28, 28, 160)  640         conv3_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_0_relu (Activation (None, 28, 28, 160)  0           conv3_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  20480       conv3_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 32)   36864       conv3_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_concat (Concatenat (None, 28, 28, 192)  0           conv3_block1_concat[0][0]        \n",
      "                                                                 conv3_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_0_bn (BatchNormali (None, 28, 28, 192)  768         conv3_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_0_relu (Activation (None, 28, 28, 192)  0           conv3_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  24576       conv3_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 32)   36864       conv3_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_concat (Concatenat (None, 28, 28, 224)  0           conv3_block2_concat[0][0]        \n",
      "                                                                 conv3_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_0_bn (BatchNormali (None, 28, 28, 224)  896         conv3_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_0_relu (Activation (None, 28, 28, 224)  0           conv3_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  28672       conv3_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_conv (Conv2D)    (None, 28, 28, 32)   36864       conv3_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_concat (Concatenat (None, 28, 28, 256)  0           conv3_block3_concat[0][0]        \n",
      "                                                                 conv3_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_0_bn (BatchNormali (None, 28, 28, 256)  1024        conv3_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_0_relu (Activation (None, 28, 28, 256)  0           conv3_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_conv (Conv2D)    (None, 28, 28, 128)  32768       conv3_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_relu (Activation (None, 28, 28, 128)  0           conv3_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_2_conv (Conv2D)    (None, 28, 28, 32)   36864       conv3_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_concat (Concatenat (None, 28, 28, 288)  0           conv3_block4_concat[0][0]        \n",
      "                                                                 conv3_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_0_bn (BatchNormali (None, 28, 28, 288)  1152        conv3_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_0_relu (Activation (None, 28, 28, 288)  0           conv3_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_conv (Conv2D)    (None, 28, 28, 128)  36864       conv3_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_relu (Activation (None, 28, 28, 128)  0           conv3_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_2_conv (Conv2D)    (None, 28, 28, 32)   36864       conv3_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_concat (Concatenat (None, 28, 28, 320)  0           conv3_block5_concat[0][0]        \n",
      "                                                                 conv3_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_0_bn (BatchNormali (None, 28, 28, 320)  1280        conv3_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_0_relu (Activation (None, 28, 28, 320)  0           conv3_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_conv (Conv2D)    (None, 28, 28, 128)  40960       conv3_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_relu (Activation (None, 28, 28, 128)  0           conv3_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_2_conv (Conv2D)    (None, 28, 28, 32)   36864       conv3_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_concat (Concatenat (None, 28, 28, 352)  0           conv3_block6_concat[0][0]        \n",
      "                                                                 conv3_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_0_bn (BatchNormali (None, 28, 28, 352)  1408        conv3_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_0_relu (Activation (None, 28, 28, 352)  0           conv3_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_conv (Conv2D)    (None, 28, 28, 128)  45056       conv3_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_relu (Activation (None, 28, 28, 128)  0           conv3_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_2_conv (Conv2D)    (None, 28, 28, 32)   36864       conv3_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_concat (Concatenat (None, 28, 28, 384)  0           conv3_block7_concat[0][0]        \n",
      "                                                                 conv3_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_0_bn (BatchNormali (None, 28, 28, 384)  1536        conv3_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_0_relu (Activation (None, 28, 28, 384)  0           conv3_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_conv (Conv2D)    (None, 28, 28, 128)  49152       conv3_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_relu (Activation (None, 28, 28, 128)  0           conv3_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_2_conv (Conv2D)    (None, 28, 28, 32)   36864       conv3_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_concat (Concatenat (None, 28, 28, 416)  0           conv3_block8_concat[0][0]        \n",
      "                                                                 conv3_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_0_bn (BatchNormal (None, 28, 28, 416)  1664        conv3_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_0_relu (Activatio (None, 28, 28, 416)  0           conv3_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_conv (Conv2D)   (None, 28, 28, 128)  53248       conv3_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_bn (BatchNormal (None, 28, 28, 128)  512         conv3_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_relu (Activatio (None, 28, 28, 128)  0           conv3_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_2_conv (Conv2D)   (None, 28, 28, 32)   36864       conv3_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_concat (Concatena (None, 28, 28, 448)  0           conv3_block9_concat[0][0]        \n",
      "                                                                 conv3_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_0_bn (BatchNormal (None, 28, 28, 448)  1792        conv3_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_0_relu (Activatio (None, 28, 28, 448)  0           conv3_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_conv (Conv2D)   (None, 28, 28, 128)  57344       conv3_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_bn (BatchNormal (None, 28, 28, 128)  512         conv3_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_relu (Activatio (None, 28, 28, 128)  0           conv3_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_2_conv (Conv2D)   (None, 28, 28, 32)   36864       conv3_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_concat (Concatena (None, 28, 28, 480)  0           conv3_block10_concat[0][0]       \n",
      "                                                                 conv3_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_0_bn (BatchNormal (None, 28, 28, 480)  1920        conv3_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_0_relu (Activatio (None, 28, 28, 480)  0           conv3_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_conv (Conv2D)   (None, 28, 28, 128)  61440       conv3_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_bn (BatchNormal (None, 28, 28, 128)  512         conv3_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_relu (Activatio (None, 28, 28, 128)  0           conv3_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_2_conv (Conv2D)   (None, 28, 28, 32)   36864       conv3_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_concat (Concatena (None, 28, 28, 512)  0           conv3_block11_concat[0][0]       \n",
      "                                                                 conv3_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool3_bn (BatchNormalization)   (None, 28, 28, 512)  2048        conv3_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool3_relu (Activation)         (None, 28, 28, 512)  0           pool3_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool3_conv (Conv2D)             (None, 28, 28, 256)  131072      pool3_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool3_pool (AveragePooling2D)   (None, 14, 14, 256)  0           pool3_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_bn (BatchNormali (None, 14, 14, 256)  1024        pool3_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_relu (Activation (None, 14, 14, 256)  0           conv4_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_conv (Conv2D)    (None, 14, 14, 128)  32768       conv4_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_bn (BatchNormali (None, 14, 14, 128)  512         conv4_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_relu (Activation (None, 14, 14, 128)  0           conv4_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 32)   36864       conv4_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_concat (Concatenat (None, 14, 14, 288)  0           pool3_pool[0][0]                 \n",
      "                                                                 conv4_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_0_bn (BatchNormali (None, 14, 14, 288)  1152        conv4_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_0_relu (Activation (None, 14, 14, 288)  0           conv4_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 128)  36864       conv4_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_bn (BatchNormali (None, 14, 14, 128)  512         conv4_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_relu (Activation (None, 14, 14, 128)  0           conv4_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 32)   36864       conv4_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_concat (Concatenat (None, 14, 14, 320)  0           conv4_block1_concat[0][0]        \n",
      "                                                                 conv4_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_0_bn (BatchNormali (None, 14, 14, 320)  1280        conv4_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_0_relu (Activation (None, 14, 14, 320)  0           conv4_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 128)  40960       conv4_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_bn (BatchNormali (None, 14, 14, 128)  512         conv4_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_relu (Activation (None, 14, 14, 128)  0           conv4_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 32)   36864       conv4_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_concat (Concatenat (None, 14, 14, 352)  0           conv4_block2_concat[0][0]        \n",
      "                                                                 conv4_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_0_bn (BatchNormali (None, 14, 14, 352)  1408        conv4_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_0_relu (Activation (None, 14, 14, 352)  0           conv4_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 128)  45056       conv4_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_bn (BatchNormali (None, 14, 14, 128)  512         conv4_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_relu (Activation (None, 14, 14, 128)  0           conv4_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 32)   36864       conv4_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_concat (Concatenat (None, 14, 14, 384)  0           conv4_block3_concat[0][0]        \n",
      "                                                                 conv4_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_0_bn (BatchNormali (None, 14, 14, 384)  1536        conv4_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_0_relu (Activation (None, 14, 14, 384)  0           conv4_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 128)  49152       conv4_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_bn (BatchNormali (None, 14, 14, 128)  512         conv4_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_relu (Activation (None, 14, 14, 128)  0           conv4_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 32)   36864       conv4_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_concat (Concatenat (None, 14, 14, 416)  0           conv4_block4_concat[0][0]        \n",
      "                                                                 conv4_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_0_bn (BatchNormali (None, 14, 14, 416)  1664        conv4_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_0_relu (Activation (None, 14, 14, 416)  0           conv4_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 128)  53248       conv4_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_bn (BatchNormali (None, 14, 14, 128)  512         conv4_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_relu (Activation (None, 14, 14, 128)  0           conv4_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_conv (Conv2D)    (None, 14, 14, 32)   36864       conv4_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_concat (Concatenat (None, 14, 14, 448)  0           conv4_block5_concat[0][0]        \n",
      "                                                                 conv4_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_0_bn (BatchNormali (None, 14, 14, 448)  1792        conv4_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_0_relu (Activation (None, 14, 14, 448)  0           conv4_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_conv (Conv2D)    (None, 14, 14, 128)  57344       conv4_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_bn (BatchNormali (None, 14, 14, 128)  512         conv4_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_relu (Activation (None, 14, 14, 128)  0           conv4_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_2_conv (Conv2D)    (None, 14, 14, 32)   36864       conv4_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_concat (Concatenat (None, 14, 14, 480)  0           conv4_block6_concat[0][0]        \n",
      "                                                                 conv4_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_0_bn (BatchNormali (None, 14, 14, 480)  1920        conv4_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_0_relu (Activation (None, 14, 14, 480)  0           conv4_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_conv (Conv2D)    (None, 14, 14, 128)  61440       conv4_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_bn (BatchNormali (None, 14, 14, 128)  512         conv4_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_relu (Activation (None, 14, 14, 128)  0           conv4_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_2_conv (Conv2D)    (None, 14, 14, 32)   36864       conv4_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_concat (Concatenat (None, 14, 14, 512)  0           conv4_block7_concat[0][0]        \n",
      "                                                                 conv4_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_0_bn (BatchNormali (None, 14, 14, 512)  2048        conv4_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_0_relu (Activation (None, 14, 14, 512)  0           conv4_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_conv (Conv2D)    (None, 14, 14, 128)  65536       conv4_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_bn (BatchNormali (None, 14, 14, 128)  512         conv4_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_relu (Activation (None, 14, 14, 128)  0           conv4_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_2_conv (Conv2D)    (None, 14, 14, 32)   36864       conv4_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_concat (Concatenat (None, 14, 14, 544)  0           conv4_block8_concat[0][0]        \n",
      "                                                                 conv4_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_0_bn (BatchNormal (None, 14, 14, 544)  2176        conv4_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_0_relu (Activatio (None, 14, 14, 544)  0           conv4_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_conv (Conv2D)   (None, 14, 14, 128)  69632       conv4_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_concat (Concatena (None, 14, 14, 576)  0           conv4_block9_concat[0][0]        \n",
      "                                                                 conv4_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_0_bn (BatchNormal (None, 14, 14, 576)  2304        conv4_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_0_relu (Activatio (None, 14, 14, 576)  0           conv4_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_conv (Conv2D)   (None, 14, 14, 128)  73728       conv4_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_concat (Concatena (None, 14, 14, 608)  0           conv4_block10_concat[0][0]       \n",
      "                                                                 conv4_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_0_bn (BatchNormal (None, 14, 14, 608)  2432        conv4_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_0_relu (Activatio (None, 14, 14, 608)  0           conv4_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_conv (Conv2D)   (None, 14, 14, 128)  77824       conv4_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_concat (Concatena (None, 14, 14, 640)  0           conv4_block11_concat[0][0]       \n",
      "                                                                 conv4_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_0_bn (BatchNormal (None, 14, 14, 640)  2560        conv4_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_0_relu (Activatio (None, 14, 14, 640)  0           conv4_block13_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_conv (Conv2D)   (None, 14, 14, 128)  81920       conv4_block13_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block13_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block13_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block13_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_concat (Concatena (None, 14, 14, 672)  0           conv4_block12_concat[0][0]       \n",
      "                                                                 conv4_block13_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_0_bn (BatchNormal (None, 14, 14, 672)  2688        conv4_block13_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_0_relu (Activatio (None, 14, 14, 672)  0           conv4_block14_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_conv (Conv2D)   (None, 14, 14, 128)  86016       conv4_block14_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block14_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block14_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block14_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_concat (Concatena (None, 14, 14, 704)  0           conv4_block13_concat[0][0]       \n",
      "                                                                 conv4_block14_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_0_bn (BatchNormal (None, 14, 14, 704)  2816        conv4_block14_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_0_relu (Activatio (None, 14, 14, 704)  0           conv4_block15_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_conv (Conv2D)   (None, 14, 14, 128)  90112       conv4_block15_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block15_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block15_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block15_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_concat (Concatena (None, 14, 14, 736)  0           conv4_block14_concat[0][0]       \n",
      "                                                                 conv4_block15_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_0_bn (BatchNormal (None, 14, 14, 736)  2944        conv4_block15_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_0_relu (Activatio (None, 14, 14, 736)  0           conv4_block16_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_conv (Conv2D)   (None, 14, 14, 128)  94208       conv4_block16_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block16_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block16_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block16_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_concat (Concatena (None, 14, 14, 768)  0           conv4_block15_concat[0][0]       \n",
      "                                                                 conv4_block16_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_0_bn (BatchNormal (None, 14, 14, 768)  3072        conv4_block16_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_0_relu (Activatio (None, 14, 14, 768)  0           conv4_block17_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_conv (Conv2D)   (None, 14, 14, 128)  98304       conv4_block17_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block17_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block17_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block17_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_concat (Concatena (None, 14, 14, 800)  0           conv4_block16_concat[0][0]       \n",
      "                                                                 conv4_block17_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_0_bn (BatchNormal (None, 14, 14, 800)  3200        conv4_block17_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_0_relu (Activatio (None, 14, 14, 800)  0           conv4_block18_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_conv (Conv2D)   (None, 14, 14, 128)  102400      conv4_block18_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block18_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block18_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block18_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_concat (Concatena (None, 14, 14, 832)  0           conv4_block17_concat[0][0]       \n",
      "                                                                 conv4_block18_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_0_bn (BatchNormal (None, 14, 14, 832)  3328        conv4_block18_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_0_relu (Activatio (None, 14, 14, 832)  0           conv4_block19_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_conv (Conv2D)   (None, 14, 14, 128)  106496      conv4_block19_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block19_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block19_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block19_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_concat (Concatena (None, 14, 14, 864)  0           conv4_block18_concat[0][0]       \n",
      "                                                                 conv4_block19_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_0_bn (BatchNormal (None, 14, 14, 864)  3456        conv4_block19_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_0_relu (Activatio (None, 14, 14, 864)  0           conv4_block20_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_conv (Conv2D)   (None, 14, 14, 128)  110592      conv4_block20_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block20_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block20_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block20_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_concat (Concatena (None, 14, 14, 896)  0           conv4_block19_concat[0][0]       \n",
      "                                                                 conv4_block20_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_0_bn (BatchNormal (None, 14, 14, 896)  3584        conv4_block20_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_0_relu (Activatio (None, 14, 14, 896)  0           conv4_block21_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_conv (Conv2D)   (None, 14, 14, 128)  114688      conv4_block21_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block21_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block21_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block21_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_concat (Concatena (None, 14, 14, 928)  0           conv4_block20_concat[0][0]       \n",
      "                                                                 conv4_block21_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_0_bn (BatchNormal (None, 14, 14, 928)  3712        conv4_block21_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_0_relu (Activatio (None, 14, 14, 928)  0           conv4_block22_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_conv (Conv2D)   (None, 14, 14, 128)  118784      conv4_block22_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block22_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block22_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block22_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_concat (Concatena (None, 14, 14, 960)  0           conv4_block21_concat[0][0]       \n",
      "                                                                 conv4_block22_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_0_bn (BatchNormal (None, 14, 14, 960)  3840        conv4_block22_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_0_relu (Activatio (None, 14, 14, 960)  0           conv4_block23_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_conv (Conv2D)   (None, 14, 14, 128)  122880      conv4_block23_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block23_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block23_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block23_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_concat (Concatena (None, 14, 14, 992)  0           conv4_block22_concat[0][0]       \n",
      "                                                                 conv4_block23_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_0_bn (BatchNormal (None, 14, 14, 992)  3968        conv4_block23_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_0_relu (Activatio (None, 14, 14, 992)  0           conv4_block24_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_conv (Conv2D)   (None, 14, 14, 128)  126976      conv4_block24_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_bn (BatchNormal (None, 14, 14, 128)  512         conv4_block24_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_relu (Activatio (None, 14, 14, 128)  0           conv4_block24_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_2_conv (Conv2D)   (None, 14, 14, 32)   36864       conv4_block24_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_concat (Concatena (None, 14, 14, 1024) 0           conv4_block23_concat[0][0]       \n",
      "                                                                 conv4_block24_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool4_bn (BatchNormalization)   (None, 14, 14, 1024) 4096        conv4_block24_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool4_relu (Activation)         (None, 14, 14, 1024) 0           pool4_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool4_conv (Conv2D)             (None, 14, 14, 512)  524288      pool4_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool4_pool (AveragePooling2D)   (None, 7, 7, 512)    0           pool4_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_bn (BatchNormali (None, 7, 7, 512)    2048        pool4_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_relu (Activation (None, 7, 7, 512)    0           conv5_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 128)    65536       conv5_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_bn (BatchNormali (None, 7, 7, 128)    512         conv5_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_relu (Activation (None, 7, 7, 128)    0           conv5_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 32)     36864       conv5_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_concat (Concatenat (None, 7, 7, 544)    0           pool4_pool[0][0]                 \n",
      "                                                                 conv5_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_0_bn (BatchNormali (None, 7, 7, 544)    2176        conv5_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_0_relu (Activation (None, 7, 7, 544)    0           conv5_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_conv (Conv2D)    (None, 7, 7, 128)    69632       conv5_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_bn (BatchNormali (None, 7, 7, 128)    512         conv5_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_relu (Activation (None, 7, 7, 128)    0           conv5_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 32)     36864       conv5_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_concat (Concatenat (None, 7, 7, 576)    0           conv5_block1_concat[0][0]        \n",
      "                                                                 conv5_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_0_bn (BatchNormali (None, 7, 7, 576)    2304        conv5_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_0_relu (Activation (None, 7, 7, 576)    0           conv5_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 128)    73728       conv5_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_bn (BatchNormali (None, 7, 7, 128)    512         conv5_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_relu (Activation (None, 7, 7, 128)    0           conv5_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 32)     36864       conv5_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_concat (Concatenat (None, 7, 7, 608)    0           conv5_block2_concat[0][0]        \n",
      "                                                                 conv5_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_0_bn (BatchNormali (None, 7, 7, 608)    2432        conv5_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_0_relu (Activation (None, 7, 7, 608)    0           conv5_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_conv (Conv2D)    (None, 7, 7, 128)    77824       conv5_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_bn (BatchNormali (None, 7, 7, 128)    512         conv5_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_relu (Activation (None, 7, 7, 128)    0           conv5_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_2_conv (Conv2D)    (None, 7, 7, 32)     36864       conv5_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_concat (Concatenat (None, 7, 7, 640)    0           conv5_block3_concat[0][0]        \n",
      "                                                                 conv5_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_0_bn (BatchNormali (None, 7, 7, 640)    2560        conv5_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_0_relu (Activation (None, 7, 7, 640)    0           conv5_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_conv (Conv2D)    (None, 7, 7, 128)    81920       conv5_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_bn (BatchNormali (None, 7, 7, 128)    512         conv5_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_relu (Activation (None, 7, 7, 128)    0           conv5_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_2_conv (Conv2D)    (None, 7, 7, 32)     36864       conv5_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_concat (Concatenat (None, 7, 7, 672)    0           conv5_block4_concat[0][0]        \n",
      "                                                                 conv5_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_0_bn (BatchNormali (None, 7, 7, 672)    2688        conv5_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_0_relu (Activation (None, 7, 7, 672)    0           conv5_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_conv (Conv2D)    (None, 7, 7, 128)    86016       conv5_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_bn (BatchNormali (None, 7, 7, 128)    512         conv5_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_relu (Activation (None, 7, 7, 128)    0           conv5_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_2_conv (Conv2D)    (None, 7, 7, 32)     36864       conv5_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_concat (Concatenat (None, 7, 7, 704)    0           conv5_block5_concat[0][0]        \n",
      "                                                                 conv5_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_0_bn (BatchNormali (None, 7, 7, 704)    2816        conv5_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_0_relu (Activation (None, 7, 7, 704)    0           conv5_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_conv (Conv2D)    (None, 7, 7, 128)    90112       conv5_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_bn (BatchNormali (None, 7, 7, 128)    512         conv5_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_relu (Activation (None, 7, 7, 128)    0           conv5_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_2_conv (Conv2D)    (None, 7, 7, 32)     36864       conv5_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_concat (Concatenat (None, 7, 7, 736)    0           conv5_block6_concat[0][0]        \n",
      "                                                                 conv5_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_0_bn (BatchNormali (None, 7, 7, 736)    2944        conv5_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_0_relu (Activation (None, 7, 7, 736)    0           conv5_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_conv (Conv2D)    (None, 7, 7, 128)    94208       conv5_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_bn (BatchNormali (None, 7, 7, 128)    512         conv5_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_relu (Activation (None, 7, 7, 128)    0           conv5_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_2_conv (Conv2D)    (None, 7, 7, 32)     36864       conv5_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_concat (Concatenat (None, 7, 7, 768)    0           conv5_block7_concat[0][0]        \n",
      "                                                                 conv5_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_0_bn (BatchNormali (None, 7, 7, 768)    3072        conv5_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_0_relu (Activation (None, 7, 7, 768)    0           conv5_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_conv (Conv2D)    (None, 7, 7, 128)    98304       conv5_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_bn (BatchNormali (None, 7, 7, 128)    512         conv5_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_relu (Activation (None, 7, 7, 128)    0           conv5_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_2_conv (Conv2D)    (None, 7, 7, 32)     36864       conv5_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_concat (Concatenat (None, 7, 7, 800)    0           conv5_block8_concat[0][0]        \n",
      "                                                                 conv5_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_0_bn (BatchNormal (None, 7, 7, 800)    3200        conv5_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_0_relu (Activatio (None, 7, 7, 800)    0           conv5_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_conv (Conv2D)   (None, 7, 7, 128)    102400      conv5_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_bn (BatchNormal (None, 7, 7, 128)    512         conv5_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_relu (Activatio (None, 7, 7, 128)    0           conv5_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_2_conv (Conv2D)   (None, 7, 7, 32)     36864       conv5_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_concat (Concatena (None, 7, 7, 832)    0           conv5_block9_concat[0][0]        \n",
      "                                                                 conv5_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_0_bn (BatchNormal (None, 7, 7, 832)    3328        conv5_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_0_relu (Activatio (None, 7, 7, 832)    0           conv5_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_conv (Conv2D)   (None, 7, 7, 128)    106496      conv5_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_bn (BatchNormal (None, 7, 7, 128)    512         conv5_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_relu (Activatio (None, 7, 7, 128)    0           conv5_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_2_conv (Conv2D)   (None, 7, 7, 32)     36864       conv5_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_concat (Concatena (None, 7, 7, 864)    0           conv5_block10_concat[0][0]       \n",
      "                                                                 conv5_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_0_bn (BatchNormal (None, 7, 7, 864)    3456        conv5_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_0_relu (Activatio (None, 7, 7, 864)    0           conv5_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_conv (Conv2D)   (None, 7, 7, 128)    110592      conv5_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_bn (BatchNormal (None, 7, 7, 128)    512         conv5_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_relu (Activatio (None, 7, 7, 128)    0           conv5_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_2_conv (Conv2D)   (None, 7, 7, 32)     36864       conv5_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_concat (Concatena (None, 7, 7, 896)    0           conv5_block11_concat[0][0]       \n",
      "                                                                 conv5_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_0_bn (BatchNormal (None, 7, 7, 896)    3584        conv5_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_0_relu (Activatio (None, 7, 7, 896)    0           conv5_block13_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_conv (Conv2D)   (None, 7, 7, 128)    114688      conv5_block13_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_bn (BatchNormal (None, 7, 7, 128)    512         conv5_block13_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_relu (Activatio (None, 7, 7, 128)    0           conv5_block13_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_2_conv (Conv2D)   (None, 7, 7, 32)     36864       conv5_block13_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_concat (Concatena (None, 7, 7, 928)    0           conv5_block12_concat[0][0]       \n",
      "                                                                 conv5_block13_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_0_bn (BatchNormal (None, 7, 7, 928)    3712        conv5_block13_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_0_relu (Activatio (None, 7, 7, 928)    0           conv5_block14_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_conv (Conv2D)   (None, 7, 7, 128)    118784      conv5_block14_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_bn (BatchNormal (None, 7, 7, 128)    512         conv5_block14_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_relu (Activatio (None, 7, 7, 128)    0           conv5_block14_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_2_conv (Conv2D)   (None, 7, 7, 32)     36864       conv5_block14_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_concat (Concatena (None, 7, 7, 960)    0           conv5_block13_concat[0][0]       \n",
      "                                                                 conv5_block14_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_0_bn (BatchNormal (None, 7, 7, 960)    3840        conv5_block14_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_0_relu (Activatio (None, 7, 7, 960)    0           conv5_block15_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_conv (Conv2D)   (None, 7, 7, 128)    122880      conv5_block15_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_bn (BatchNormal (None, 7, 7, 128)    512         conv5_block15_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_relu (Activatio (None, 7, 7, 128)    0           conv5_block15_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_2_conv (Conv2D)   (None, 7, 7, 32)     36864       conv5_block15_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_concat (Concatena (None, 7, 7, 992)    0           conv5_block14_concat[0][0]       \n",
      "                                                                 conv5_block15_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_0_bn (BatchNormal (None, 7, 7, 992)    3968        conv5_block15_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_0_relu (Activatio (None, 7, 7, 992)    0           conv5_block16_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_conv (Conv2D)   (None, 7, 7, 128)    126976      conv5_block16_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_bn (BatchNormal (None, 7, 7, 128)    512         conv5_block16_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_relu (Activatio (None, 7, 7, 128)    0           conv5_block16_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_2_conv (Conv2D)   (None, 7, 7, 32)     36864       conv5_block16_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_concat (Concatena (None, 7, 7, 1024)   0           conv5_block15_concat[0][0]       \n",
      "                                                                 conv5_block16_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "bn (BatchNormalization)         (None, 7, 7, 1024)   4096        conv5_block16_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "relu (Activation)               (None, 7, 7, 1024)   0           bn[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 1024)         0           relu[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "predictions (Dense)             (None, 1000)         1025000     avg_pool[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 8,062,504\n",
      "Trainable params: 7,978,856\n",
      "Non-trainable params: 83,648\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "这里我使用 DenseNet121 模型\n",
    "这一文章就先采用默认的1000类（classes=1000）\n",
    "下一篇文章的时候我再讲解no-top与自定义类别数\n",
    "\"\"\"\n",
    "model = tf.keras.applications.DenseNet121(weights='imagenet')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、设置动态学习率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里先罗列一下学习率大与学习率小的优缺点。\n",
    "\n",
    "- 学习率大\n",
    "    - 优点：\n",
    "        1、加快学习速率。\n",
    "        2、有助于跳出局部最优值。\n",
    "    - 缺点：\n",
    "        1、导致模型训练不收敛。\n",
    "        2、单单使用大学习率容易导致模型不精确。\n",
    "\n",
    "- 学习率小\n",
    "    - 优点：\n",
    "        1、有助于模型收敛、模型细化。\n",
    "        2、提高模型精度。\n",
    "    - 缺点：\n",
    "        1、很难跳出局部最优值。\n",
    "        2、收敛缓慢。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：这里设置的动态学习率为：指数衰减型（ExponentialDecay）。在每一个epoch开始前，学习率（learning_rate）都将会重置为初始学习率（initial_learning_rate），然后再重新开始衰减。计算公式如下：\n",
    "\n",
    ">learning_rate = initial_learning_rate * decay_rate ^ (step / decay_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置初始学习率\n",
    "initial_learning_rate = 1e-4\n",
    "\n",
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "        initial_learning_rate, \n",
    "        decay_steps=5,      # 敲黑板！！！这里是指 steps，不是指epochs\n",
    "        decay_rate=0.96,     # lr经过一次衰减就会变成 decay_rate*lr\n",
    "        staircase=True)\n",
    "\n",
    "# 将指数衰减学习率送入优化器\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 五、编译"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在准备对模型进行训练之前，还需要再对其进行一些设置。以下内容是在模型的编译步骤中添加的：\n",
    "\n",
    "- 损失函数（loss）：用于衡量模型在训练期间的准确率。\n",
    "- 优化器（optimizer）：决定模型如何根据其看到的数据和自身的损失函数进行更新。\n",
    "- 指标（metrics）：用于监控训练和测试步骤。以下示例使用了准确率，即被正确分类的图像的比率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=optimizer,\n",
    "              loss     ='sparse_categorical_crossentropy',\n",
    "              metrics  =['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 六、训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "22/22 [==============================] - 31s 158ms/step - loss: 3.9512 - accuracy: 0.4489 - val_loss: 3.3925 - val_accuracy: 0.3721\n",
      "Epoch 2/20\n",
      "22/22 [==============================] - 2s 77ms/step - loss: 0.1809 - accuracy: 0.9830 - val_loss: 1.9162 - val_accuracy: 0.6047\n",
      "Epoch 3/20\n",
      "22/22 [==============================] - 2s 77ms/step - loss: 0.0229 - accuracy: 1.0000 - val_loss: 1.1465 - val_accuracy: 0.7674\n",
      "Epoch 4/20\n",
      "22/22 [==============================] - 2s 76ms/step - loss: 0.0117 - accuracy: 1.0000 - val_loss: 0.8228 - val_accuracy: 0.8837\n",
      "Epoch 5/20\n",
      "22/22 [==============================] - 2s 76ms/step - loss: 0.0093 - accuracy: 1.0000 - val_loss: 0.6380 - val_accuracy: 0.9070\n",
      "Epoch 6/20\n",
      "22/22 [==============================] - 2s 77ms/step - loss: 0.0082 - accuracy: 1.0000 - val_loss: 0.5275 - val_accuracy: 0.9302\n",
      "Epoch 7/20\n",
      "22/22 [==============================] - 2s 79ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.4580 - val_accuracy: 0.9535\n",
      "Epoch 8/20\n",
      "22/22 [==============================] - 2s 78ms/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.4133 - val_accuracy: 0.9535\n",
      "Epoch 9/20\n",
      "22/22 [==============================] - 2s 78ms/step - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.3803 - val_accuracy: 0.9535\n",
      "Epoch 10/20\n",
      "22/22 [==============================] - 2s 79ms/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 0.3586 - val_accuracy: 0.9535\n",
      "Epoch 11/20\n",
      "22/22 [==============================] - 2s 78ms/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.3420 - val_accuracy: 0.9767\n",
      "Epoch 12/20\n",
      "22/22 [==============================] - 2s 80ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.3282 - val_accuracy: 0.9767\n",
      "Epoch 13/20\n",
      "22/22 [==============================] - 2s 77ms/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.3170 - val_accuracy: 0.9767\n",
      "Epoch 14/20\n",
      "22/22 [==============================] - 2s 77ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.3078 - val_accuracy: 0.9767\n",
      "Epoch 15/20\n",
      "22/22 [==============================] - 2s 76ms/step - loss: 0.0052 - accuracy: 1.0000 - val_loss: 0.3012 - val_accuracy: 0.9767\n",
      "Epoch 16/20\n",
      "22/22 [==============================] - 2s 76ms/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9767\n",
      "Epoch 17/20\n",
      "22/22 [==============================] - 2s 77ms/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.2920 - val_accuracy: 0.9767\n",
      "Epoch 18/20\n",
      "22/22 [==============================] - 2s 76ms/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.2888 - val_accuracy: 0.9767\n",
      "Epoch 19/20\n",
      "22/22 [==============================] - 2s 76ms/step - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.2859 - val_accuracy: 0.9767\n",
      "Epoch 20/20\n",
      "22/22 [==============================] - 2s 76ms/step - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.2835 - val_accuracy: 0.9767\n"
     ]
    }
   ],
   "source": [
    "epochs = 20\n",
    "\n",
    "history = model.fit(\n",
    "    train_ds,\n",
    "    validation_data=val_ds,\n",
    "    epochs=epochs\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 七、模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs_range = range(epochs)\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "\n",
    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, loss, label='Training Loss')\n",
    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 八、保存and加载模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这是最简单的模型保存与加载方法哈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存模型\n",
    "model.save('model/16_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载模型\n",
    "new_model = tf.keras.models.load_model('model/16_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 九、预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 采用加载的模型（new_model）来看预测结果\n",
    "\n",
    "plt.figure(figsize=(10, 5))  # 图形的宽为10高为5\n",
    "plt.suptitle(\"预测结果展示\")\n",
    "\n",
    "for images, labels in val_ds.take(1):\n",
    "    for i in range(8):\n",
    "        ax = plt.subplot(2, 4, i + 1)  \n",
    "        \n",
    "        # 显示图片\n",
    "        plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
    "        \n",
    "        # 需要给图片增加一个维度\n",
    "        img_array = tf.expand_dims(images[i], 0) \n",
    "        \n",
    "        # 使用模型预测图片中的人物\n",
    "        predictions = new_model.predict(img_array)\n",
    "        plt.title(class_names[np.argmax(predictions)])\n",
    "\n",
    "        plt.axis(\"off\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8rc1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "212px",
    "width": "160px"
   },
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "180px"
   },
   "toc_section_display": true,
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